CTML

Welcome to CTML

Rio de Janeiro, Brazil | Nov. 27-29, 2026

International Conference on Computational Theory and Machine Learning

The International Conference on Computational Theory and Machine Learning (CTML 2026) will be held in Rio de Janeiro, Brazil, during November 27-29, 2026. This conference focuses on computational theory, machine learning foundations, algorithm design, and related interdisciplinary research. It provides a high-level academic platform for global researchers, scholars, and practitioners to share innovative findings, exchange ideas, and promote academic cooperation. CTML 2026 welcomes high-quality papers and presentations, aiming to advance theoretical progress and practical applications in computational theory and machine learning.

Sponsor Organizer

Proceedings
All submissions will be sent for double-blind review. Accepted papers with registration and presentation will be published in Conference Proceedings.

Important Dates

Submission Deadline: June 30, 2026
Notification Date: July 30, 2026
Registration Deadline: August 15, 2026
Camera Ready: August 15, 2026

Submission Method

Submissions including full papers or abstracts should be submitted via Electronic Submission System. Please contact email if you have any questions during submission.

Registration

To be considered for final acceptance and publication, each paper MUST have at least one paid full registration by the registration deadline. Papers that do not adhere to this requirement will be excluded from the proceedings.

Reasons to Join CTML 2026

Access cutting-edge insights from world-renowned keynote speakers and technical sessions on computational theory and machine learning.
Share your original research with a global academic audience and receive professional feedback from field experts.
Expand your academic and industrial network by connecting with researchers, scholars, and practitioners worldwide.
Enhance your research visibility and academic profile through formal publication and presentation at an international conference.

Conference Awards

Best Paper Award
Excellent Oral Presentation Award
Excellent Poster Presentation Award

Download

The authors are suggested to use the CTML Formatting Template to meet formatting requirements.

Word Template Download
LaTeX Template Download

Rio de Janeiro, Brazil

Rio de Janeiro blends iconic landmarks, natural beauty, and lively culture. Don't miss Christ the Redeemer, Sugarloaf Mountain's cable car, and the golden sands of Copacabana and Ipanema for an unforgettable Brazilian experience.

Christ the Redeemer & Corcovado Mountain

Visit the iconic New Seven Wonders statue, take the cog train through Tijuca Forest, and enjoy 360 degree city views.

Sugarloaf Mountain Cable Car

Ride the historic cable car to the summit for stunning sunset views over Guanabara Bay and Rio's skyline.

Copacabana & Ipanema Beaches

Relax on world-famous shores, stroll the promenade, and soak up Rio's vibrant beach culture.

Call for Papers - Topics

Track 1: Foundations of Computational Learning

Statistical Learning Theory and Generalization
Computational Complexity of Learning
Online Learning and Regret Analysis
Learning Dynamics and Convergence
Scaling Laws and Emergent Behavior in Large Models
PAC-Bayes Theory and Algorithmic Stability

Track 2: Deep Learning Theory and Neural Architectures

Expressivity and Capacity of Neural Networks
Theoretical Analysis of Transformers and Foundation Models
Neural Network Optimization Landscapes
Overparameterization and Double Descent
Implicit Regularization and Bias of Gradient Methods
Mechanistic Interpretability and Model Internals
Physics-Informed Neural Networks and Neural Operators

Track 3: Optimization and Algorithms for Machine Learning

Convex and Non-Convex Optimization
Evolutionary Algorithms and Metaheuristics
Combinatorial Optimization in Learning
Surrogate-Assisted and Expensive Optimization
Optimization for Resource-Constrained Settings
Federated Learning and Distributed Optimization
Multi-Objective Optimization in ML Systems

Track 4: Graph Theory, Combinatorics, and Learning on Structures

Graph Neural Networks Theory
Spectral Graph Theory and Applications
Algorithmic Graph Theory and Network Analysis
Combinatorial Optimization with Learning
Random Graphs and Probabilistic Methods
Geometric Deep Learning
Learning on Manifolds and Non-Euclidean Data

Track 5: Trustworthy and Explainable AI

Causal Inference and Discovery
Explainability and Interpretability
Robustness, Uncertainty, and Calibration
Privacy and Fairness in Machine Learning
Adversarial Machine Learning
Distribution Shift and Domain Generalization
Safety and Alignment of AI Systems

Track 6: AI for Scientific Discovery and Emerging Frontiers

AI for Scientific Discovery
Quantum Machine Learning
Symbolic Regression and Scientific Law Discovery
AI-Accelerated Scientific Computing
Multi-Modal Learning and Fusion
Computational Biology and AI for Healthcare
Climate Modeling and Environmental AI

Track 7: Efficient and Scalable Machine Learning Systems

Model Compression and Knowledge Distillation
Quantization and Pruning
Neural Architecture Search
Edge AI and TinyML
Green AI and Energy-Efficient Learning
Large-Scale Training Systems
ML Compilers and Hardware-Software Co-Design